Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations51290
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.2 MiB
Average record size in memory168.0 B

Variable types

Text6
Unsupported2
Categorical8
Numeric5

Alerts

category is highly overall correlated with sub_categoryHigh correlation
discount is highly overall correlated with profitHigh correlation
market is highly overall correlated with regionHigh correlation
profit is highly overall correlated with discountHigh correlation
region is highly overall correlated with marketHigh correlation
sales is highly overall correlated with shipping_costHigh correlation
shipping_cost is highly overall correlated with salesHigh correlation
sub_category is highly overall correlated with categoryHigh correlation
order_date is an unsupported type, check if it needs cleaning or further analysis Unsupported
ship_date is an unsupported type, check if it needs cleaning or further analysis Unsupported
discount has 29009 (56.6%) zeros Zeros
profit has 668 (1.3%) zeros Zeros

Reproduction

Analysis started2025-10-14 07:21:58.630641
Analysis finished2025-10-14 07:22:14.799347
Duration16.17 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct25035
Distinct (%)48.8%
Missing0
Missing (%)0.0%
Memory size400.8 KiB
2025-10-14T10:22:15.270040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length14
Mean length13.569643
Min length9

Characters and Unicode

Total characters695987
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12257 ?
Unique (%)23.9%

Sample

1st rowAG-2011-2040
2nd rowIN-2011-47883
3rd rowHU-2011-1220
4th rowIT-2011-3647632
5th rowIN-2011-47883
ValueCountFrequency (%)
ca-2014-100111 14
 
< 0.1%
to-2014-9950 13
 
< 0.1%
ni-2014-8880 13
 
< 0.1%
in-2013-42311 13
 
< 0.1%
in-2012-41261 13
 
< 0.1%
mx-2014-166541 13
 
< 0.1%
in-2011-76625 12
 
< 0.1%
es-2012-5776825 12
 
< 0.1%
in-2014-15263 12
 
< 0.1%
mx-2013-142678 12
 
< 0.1%
Other values (25025) 51163
99.8%
2025-10-14T10:22:16.059686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 108762
15.6%
- 102580
14.7%
2 90147
13.0%
0 85011
12.2%
4 45213
 
6.5%
3 41592
 
6.0%
5 27294
 
3.9%
6 25799
 
3.7%
7 23086
 
3.3%
8 22626
 
3.3%
Other values (27) 123877
17.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 695987
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 108762
15.6%
- 102580
14.7%
2 90147
13.0%
0 85011
12.2%
4 45213
 
6.5%
3 41592
 
6.0%
5 27294
 
3.9%
6 25799
 
3.7%
7 23086
 
3.3%
8 22626
 
3.3%
Other values (27) 123877
17.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 695987
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 108762
15.6%
- 102580
14.7%
2 90147
13.0%
0 85011
12.2%
4 45213
 
6.5%
3 41592
 
6.0%
5 27294
 
3.9%
6 25799
 
3.7%
7 23086
 
3.3%
8 22626
 
3.3%
Other values (27) 123877
17.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 695987
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 108762
15.6%
- 102580
14.7%
2 90147
13.0%
0 85011
12.2%
4 45213
 
6.5%
3 41592
 
6.0%
5 27294
 
3.9%
6 25799
 
3.7%
7 23086
 
3.3%
8 22626
 
3.3%
Other values (27) 123877
17.8%

order_date
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size400.8 KiB

ship_date
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size400.8 KiB

ship_mode
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size400.8 KiB
Standard Class
30775 
Second Class
10309 
First Class
7505 
Same Day
 
2701

Length

Max length14
Median length14
Mean length12.843069
Min length8

Characters and Unicode

Total characters658721
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStandard Class
2nd rowStandard Class
3rd rowSecond Class
4th rowSecond Class
5th rowStandard Class

Common Values

ValueCountFrequency (%)
Standard Class 30775
60.0%
Second Class 10309
 
20.1%
First Class 7505
 
14.6%
Same Day 2701
 
5.3%

Length

2025-10-14T10:22:16.310508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-14T10:22:16.501359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
class 48589
47.4%
standard 30775
30.0%
second 10309
 
10.0%
first 7505
 
7.3%
same 2701
 
2.6%
day 2701
 
2.6%

Most occurring characters

ValueCountFrequency (%)
a 115541
17.5%
s 104683
15.9%
d 71859
10.9%
51290
7.8%
l 48589
7.4%
C 48589
7.4%
S 43785
 
6.6%
n 41084
 
6.2%
r 38280
 
5.8%
t 38280
 
5.8%
Other values (8) 56741
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 658721
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 115541
17.5%
s 104683
15.9%
d 71859
10.9%
51290
7.8%
l 48589
7.4%
C 48589
7.4%
S 43785
 
6.6%
n 41084
 
6.2%
r 38280
 
5.8%
t 38280
 
5.8%
Other values (8) 56741
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 658721
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 115541
17.5%
s 104683
15.9%
d 71859
10.9%
51290
7.8%
l 48589
7.4%
C 48589
7.4%
S 43785
 
6.6%
n 41084
 
6.2%
r 38280
 
5.8%
t 38280
 
5.8%
Other values (8) 56741
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 658721
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 115541
17.5%
s 104683
15.9%
d 71859
10.9%
51290
7.8%
l 48589
7.4%
C 48589
7.4%
S 43785
 
6.6%
n 41084
 
6.2%
r 38280
 
5.8%
t 38280
 
5.8%
Other values (8) 56741
8.6%
Distinct795
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size400.8 KiB
2025-10-14T10:22:17.025575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22
Median length18
Mean length12.946227
Min length7

Characters and Unicode

Total characters664012
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowToby Braunhardt
2nd rowJoseph Holt
3rd rowAnnie Thurman
4th rowEugene Moren
5th rowJoseph Holt
ValueCountFrequency (%)
michael 655
 
0.6%
john 522
 
0.5%
paul 438
 
0.4%
patrick 437
 
0.4%
tom 430
 
0.4%
stewart 426
 
0.4%
anthony 424
 
0.4%
frank 422
 
0.4%
bill 402
 
0.4%
alan 402
 
0.4%
Other values (901) 98324
95.6%
2025-10-14T10:22:18.041312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 61455
 
9.3%
e 60921
 
9.2%
n 51801
 
7.8%
51592
 
7.8%
r 48359
 
7.3%
i 40342
 
6.1%
l 34229
 
5.2%
o 30793
 
4.6%
t 27197
 
4.1%
s 23187
 
3.5%
Other values (47) 234136
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 664012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 61455
 
9.3%
e 60921
 
9.2%
n 51801
 
7.8%
51592
 
7.8%
r 48359
 
7.3%
i 40342
 
6.1%
l 34229
 
5.2%
o 30793
 
4.6%
t 27197
 
4.1%
s 23187
 
3.5%
Other values (47) 234136
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 664012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 61455
 
9.3%
e 60921
 
9.2%
n 51801
 
7.8%
51592
 
7.8%
r 48359
 
7.3%
i 40342
 
6.1%
l 34229
 
5.2%
o 30793
 
4.6%
t 27197
 
4.1%
s 23187
 
3.5%
Other values (47) 234136
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 664012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 61455
 
9.3%
e 60921
 
9.2%
n 51801
 
7.8%
51592
 
7.8%
r 48359
 
7.3%
i 40342
 
6.1%
l 34229
 
5.2%
o 30793
 
4.6%
t 27197
 
4.1%
s 23187
 
3.5%
Other values (47) 234136
35.3%

segment
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size400.8 KiB
Consumer
26518 
Corporate
15429 
Home Office
9343 

Length

Max length11
Median length8
Mean length8.8472997
Min length8

Characters and Unicode

Total characters453778
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConsumer
2nd rowConsumer
3rd rowConsumer
4th rowHome Office
5th rowConsumer

Common Values

ValueCountFrequency (%)
Consumer 26518
51.7%
Corporate 15429
30.1%
Home Office 9343
 
18.2%

Length

2025-10-14T10:22:18.314161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-14T10:22:18.546493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
consumer 26518
43.7%
corporate 15429
25.4%
home 9343
 
15.4%
office 9343
 
15.4%

Most occurring characters

ValueCountFrequency (%)
o 66719
14.7%
e 60633
13.4%
r 57376
12.6%
C 41947
9.2%
m 35861
7.9%
n 26518
 
5.8%
s 26518
 
5.8%
u 26518
 
5.8%
f 18686
 
4.1%
t 15429
 
3.4%
Other values (7) 77573
17.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 453778
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 66719
14.7%
e 60633
13.4%
r 57376
12.6%
C 41947
9.2%
m 35861
7.9%
n 26518
 
5.8%
s 26518
 
5.8%
u 26518
 
5.8%
f 18686
 
4.1%
t 15429
 
3.4%
Other values (7) 77573
17.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 453778
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 66719
14.7%
e 60633
13.4%
r 57376
12.6%
C 41947
9.2%
m 35861
7.9%
n 26518
 
5.8%
s 26518
 
5.8%
u 26518
 
5.8%
f 18686
 
4.1%
t 15429
 
3.4%
Other values (7) 77573
17.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 453778
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 66719
14.7%
e 60633
13.4%
r 57376
12.6%
C 41947
9.2%
m 35861
7.9%
n 26518
 
5.8%
s 26518
 
5.8%
u 26518
 
5.8%
f 18686
 
4.1%
t 15429
 
3.4%
Other values (7) 77573
17.1%

state
Text

Distinct1094
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size400.8 KiB
2025-10-14T10:22:19.020643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length36
Median length26
Mean length9.6409826
Min length3

Characters and Unicode

Total characters494486
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)0.1%

Sample

1st rowConstantine
2nd rowNew South Wales
3rd rowBudapest
4th rowStockholm
5th rowNew South Wales
ValueCountFrequency (%)
california 2125
 
3.1%
new 2103
 
3.1%
england 1499
 
2.2%
south 1201
 
1.8%
north 1145
 
1.7%
york 1128
 
1.7%
texas 985
 
1.4%
ile-de-france 981
 
1.4%
wales 817
 
1.2%
capital 784
 
1.1%
Other values (1189) 55429
81.3%
2025-10-14T10:22:19.820403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 72974
14.8%
n 39413
 
8.0%
i 33410
 
6.8%
e 30993
 
6.3%
o 28369
 
5.7%
r 28361
 
5.7%
l 23890
 
4.8%
t 21263
 
4.3%
s 19509
 
3.9%
16907
 
3.4%
Other values (71) 179397
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 494486
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 72974
14.8%
n 39413
 
8.0%
i 33410
 
6.8%
e 30993
 
6.3%
o 28369
 
5.7%
r 28361
 
5.7%
l 23890
 
4.8%
t 21263
 
4.3%
s 19509
 
3.9%
16907
 
3.4%
Other values (71) 179397
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 494486
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 72974
14.8%
n 39413
 
8.0%
i 33410
 
6.8%
e 30993
 
6.3%
o 28369
 
5.7%
r 28361
 
5.7%
l 23890
 
4.8%
t 21263
 
4.3%
s 19509
 
3.9%
16907
 
3.4%
Other values (71) 179397
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 494486
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 72974
14.8%
n 39413
 
8.0%
i 33410
 
6.8%
e 30993
 
6.3%
o 28369
 
5.7%
r 28361
 
5.7%
l 23890
 
4.8%
t 21263
 
4.3%
s 19509
 
3.9%
16907
 
3.4%
Other values (71) 179397
36.3%
Distinct147
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size400.8 KiB
2025-10-14T10:22:20.425740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length22
Mean length8.8366738
Min length4

Characters and Unicode

Total characters453233
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlgeria
2nd rowAustralia
3rd rowHungary
4th rowSweden
5th rowAustralia
ValueCountFrequency (%)
united 11641
 
17.1%
states 9994
 
14.7%
australia 2837
 
4.2%
france 2827
 
4.2%
mexico 2644
 
3.9%
germany 2065
 
3.0%
china 1880
 
2.8%
kingdom 1633
 
2.4%
brazil 1599
 
2.3%
india 1555
 
2.3%
Other values (154) 29420
43.2%
2025-10-14T10:22:22.025100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 55137
 
12.2%
e 43176
 
9.5%
i 40575
 
9.0%
t 40485
 
8.9%
n 36881
 
8.1%
d 21302
 
4.7%
r 20390
 
4.5%
s 18355
 
4.0%
16805
 
3.7%
o 14461
 
3.2%
Other values (44) 145666
32.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 453233
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 55137
 
12.2%
e 43176
 
9.5%
i 40575
 
9.0%
t 40485
 
8.9%
n 36881
 
8.1%
d 21302
 
4.7%
r 20390
 
4.5%
s 18355
 
4.0%
16805
 
3.7%
o 14461
 
3.2%
Other values (44) 145666
32.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 453233
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 55137
 
12.2%
e 43176
 
9.5%
i 40575
 
9.0%
t 40485
 
8.9%
n 36881
 
8.1%
d 21302
 
4.7%
r 20390
 
4.5%
s 18355
 
4.0%
16805
 
3.7%
o 14461
 
3.2%
Other values (44) 145666
32.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 453233
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 55137
 
12.2%
e 43176
 
9.5%
i 40575
 
9.0%
t 40485
 
8.9%
n 36881
 
8.1%
d 21302
 
4.7%
r 20390
 
4.5%
s 18355
 
4.0%
16805
 
3.7%
o 14461
 
3.2%
Other values (44) 145666
32.1%

market
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size400.8 KiB
APAC
11002 
LATAM
10294 
EU
10000 
US
9994 
EMEA
5029 
Other values (2)
4971 

Length

Max length6
Median length5
Mean length3.6148957
Min length2

Characters and Unicode

Total characters185408
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfrica
2nd rowAPAC
3rd rowEMEA
4th rowEU
5th rowAPAC

Common Values

ValueCountFrequency (%)
APAC 11002
21.5%
LATAM 10294
20.1%
EU 10000
19.5%
US 9994
19.5%
EMEA 5029
9.8%
Africa 4587
8.9%
Canada 384
 
0.7%

Length

2025-10-14T10:22:22.304259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-14T10:22:22.552383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
apac 11002
21.5%
latam 10294
20.1%
eu 10000
19.5%
us 9994
19.5%
emea 5029
9.8%
africa 4587
8.9%
canada 384
 
0.7%

Most occurring characters

ValueCountFrequency (%)
A 52208
28.2%
E 20058
 
10.8%
U 19994
 
10.8%
M 15323
 
8.3%
C 11386
 
6.1%
P 11002
 
5.9%
L 10294
 
5.6%
T 10294
 
5.6%
S 9994
 
5.4%
a 5739
 
3.1%
Other values (6) 19116
 
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 185408
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 52208
28.2%
E 20058
 
10.8%
U 19994
 
10.8%
M 15323
 
8.3%
C 11386
 
6.1%
P 11002
 
5.9%
L 10294
 
5.6%
T 10294
 
5.6%
S 9994
 
5.4%
a 5739
 
3.1%
Other values (6) 19116
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 185408
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 52208
28.2%
E 20058
 
10.8%
U 19994
 
10.8%
M 15323
 
8.3%
C 11386
 
6.1%
P 11002
 
5.9%
L 10294
 
5.6%
T 10294
 
5.6%
S 9994
 
5.4%
a 5739
 
3.1%
Other values (6) 19116
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 185408
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 52208
28.2%
E 20058
 
10.8%
U 19994
 
10.8%
M 15323
 
8.3%
C 11386
 
6.1%
P 11002
 
5.9%
L 10294
 
5.6%
T 10294
 
5.6%
S 9994
 
5.4%
a 5739
 
3.1%
Other values (6) 19116
 
10.3%

region
Categorical

High correlation 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size400.8 KiB
Central
11117 
South
6645 
EMEA
5029 
North
4785 
Africa
4587 
Other values (8)
19127 

Length

Max length14
Median length12
Mean length6.638643
Min length4

Characters and Unicode

Total characters340496
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfrica
2nd rowOceania
3rd rowEMEA
4th rowNorth
5th rowOceania

Common Values

ValueCountFrequency (%)
Central 11117
21.7%
South 6645
13.0%
EMEA 5029
9.8%
North 4785
9.3%
Africa 4587
8.9%
Oceania 3487
 
6.8%
West 3203
 
6.2%
Southeast Asia 3129
 
6.1%
East 2848
 
5.6%
North Asia 2338
 
4.6%
Other values (3) 4122
 
8.0%

Length

2025-10-14T10:22:22.847694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
central 13165
22.4%
asia 7515
12.8%
north 7123
12.1%
south 6645
11.3%
emea 5029
 
8.6%
africa 4587
 
7.8%
oceania 3487
 
5.9%
west 3203
 
5.4%
southeast 3129
 
5.3%
east 2848
 
4.8%
Other values (2) 2074
 
3.5%

Most occurring characters

ValueCountFrequency (%)
a 42750
12.6%
t 39242
 
11.5%
r 26565
 
7.8%
e 24674
 
7.2%
n 18726
 
5.5%
i 17279
 
5.1%
A 17131
 
5.0%
o 16897
 
5.0%
h 16897
 
5.0%
s 16695
 
4.9%
Other values (14) 103640
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 340496
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 42750
12.6%
t 39242
 
11.5%
r 26565
 
7.8%
e 24674
 
7.2%
n 18726
 
5.5%
i 17279
 
5.1%
A 17131
 
5.0%
o 16897
 
5.0%
h 16897
 
5.0%
s 16695
 
4.9%
Other values (14) 103640
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 340496
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 42750
12.6%
t 39242
 
11.5%
r 26565
 
7.8%
e 24674
 
7.2%
n 18726
 
5.5%
i 17279
 
5.1%
A 17131
 
5.0%
o 16897
 
5.0%
h 16897
 
5.0%
s 16695
 
4.9%
Other values (14) 103640
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 340496
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 42750
12.6%
t 39242
 
11.5%
r 26565
 
7.8%
e 24674
 
7.2%
n 18726
 
5.5%
i 17279
 
5.1%
A 17131
 
5.0%
o 16897
 
5.0%
h 16897
 
5.0%
s 16695
 
4.9%
Other values (14) 103640
30.4%
Distinct10292
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Memory size400.8 KiB
2025-10-14T10:22:23.306853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length16
Median length15
Mean length15.195009
Min length15

Characters and Unicode

Total characters779352
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1420 ?
Unique (%)2.8%

Sample

1st rowOFF-TEN-10000025
2nd rowOFF-SU-10000618
3rd rowOFF-TEN-10001585
4th rowOFF-PA-10001492
5th rowFUR-FU-10003447
ValueCountFrequency (%)
tec-hp 83
 
0.2%
off-ar-10003651 35
 
0.1%
off-ar-10003829 31
 
0.1%
off-bi-10002799 30
 
0.1%
off-bi-10003708 30
 
0.1%
fur-ch-10003354 28
 
0.1%
off-bi-10002570 27
 
0.1%
off-bi-10004140 25
 
< 0.1%
off-bi-10004632 24
 
< 0.1%
off-bi-10001808 24
 
< 0.1%
Other values (10283) 51036
99.3%
2025-10-14T10:22:23.978236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 179614
23.0%
- 102580
13.2%
F 77961
10.0%
1 77220
9.9%
O 37143
 
4.8%
2 25593
 
3.3%
3 25555
 
3.3%
4 25148
 
3.2%
A 20235
 
2.6%
C 19282
 
2.5%
Other values (25) 189021
24.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 779352
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 179614
23.0%
- 102580
13.2%
F 77961
10.0%
1 77220
9.9%
O 37143
 
4.8%
2 25593
 
3.3%
3 25555
 
3.3%
4 25148
 
3.2%
A 20235
 
2.6%
C 19282
 
2.5%
Other values (25) 189021
24.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 779352
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 179614
23.0%
- 102580
13.2%
F 77961
10.0%
1 77220
9.9%
O 37143
 
4.8%
2 25593
 
3.3%
3 25555
 
3.3%
4 25148
 
3.2%
A 20235
 
2.6%
C 19282
 
2.5%
Other values (25) 189021
24.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 779352
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 179614
23.0%
- 102580
13.2%
F 77961
10.0%
1 77220
9.9%
O 37143
 
4.8%
2 25593
 
3.3%
3 25555
 
3.3%
4 25148
 
3.2%
A 20235
 
2.6%
C 19282
 
2.5%
Other values (25) 189021
24.3%

category
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size400.8 KiB
Office Supplies
31273 
Technology
10141 
Furniture
9876 

Length

Max length15
Median length15
Mean length12.856093
Min length9

Characters and Unicode

Total characters659389
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOffice Supplies
2nd rowOffice Supplies
3rd rowOffice Supplies
4th rowOffice Supplies
5th rowFurniture

Common Values

ValueCountFrequency (%)
Office Supplies 31273
61.0%
Technology 10141
 
19.8%
Furniture 9876
 
19.3%

Length

2025-10-14T10:22:24.251877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-14T10:22:24.460483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
office 31273
37.9%
supplies 31273
37.9%
technology 10141
 
12.3%
furniture 9876
 
12.0%

Most occurring characters

ValueCountFrequency (%)
e 82563
12.5%
i 72422
11.0%
p 62546
9.5%
f 62546
9.5%
u 51025
 
7.7%
c 41414
 
6.3%
l 41414
 
6.3%
O 31273
 
4.7%
s 31273
 
4.7%
S 31273
 
4.7%
Other values (10) 151640
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 659389
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 82563
12.5%
i 72422
11.0%
p 62546
9.5%
f 62546
9.5%
u 51025
 
7.7%
c 41414
 
6.3%
l 41414
 
6.3%
O 31273
 
4.7%
s 31273
 
4.7%
S 31273
 
4.7%
Other values (10) 151640
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 659389
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 82563
12.5%
i 72422
11.0%
p 62546
9.5%
f 62546
9.5%
u 51025
 
7.7%
c 41414
 
6.3%
l 41414
 
6.3%
O 31273
 
4.7%
s 31273
 
4.7%
S 31273
 
4.7%
Other values (10) 151640
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 659389
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 82563
12.5%
i 72422
11.0%
p 62546
9.5%
f 62546
9.5%
u 51025
 
7.7%
c 41414
 
6.3%
l 41414
 
6.3%
O 31273
 
4.7%
s 31273
 
4.7%
S 31273
 
4.7%
Other values (10) 151640
23.0%

sub_category
Categorical

High correlation 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size400.8 KiB
Binders
6152 
Storage
5059 
Art
4883 
Paper
3538 
Chairs
3434 
Other values (12)
28224 

Length

Max length11
Median length9
Mean length7.2304933
Min length3

Characters and Unicode

Total characters370852
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStorage
2nd rowSupplies
3rd rowStorage
4th rowPaper
5th rowFurnishings

Common Values

ValueCountFrequency (%)
Binders 6152
12.0%
Storage 5059
 
9.9%
Art 4883
 
9.5%
Paper 3538
 
6.9%
Chairs 3434
 
6.7%
Phones 3357
 
6.5%
Furnishings 3170
 
6.2%
Accessories 3075
 
6.0%
Labels 2606
 
5.1%
Envelopes 2435
 
4.7%
Other values (7) 13581
26.5%

Length

2025-10-14T10:22:24.765753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
binders 6152
12.0%
storage 5059
 
9.9%
art 4883
 
9.5%
paper 3538
 
6.9%
chairs 3434
 
6.7%
phones 3357
 
6.5%
furnishings 3170
 
6.2%
accessories 3075
 
6.0%
labels 2606
 
5.1%
envelopes 2435
 
4.7%
Other values (7) 13581
26.5%

Most occurring characters

ValueCountFrequency (%)
s 51961
14.0%
e 47733
12.9%
r 33954
 
9.2%
i 26890
 
7.3%
n 23945
 
6.5%
a 23570
 
6.4%
o 20971
 
5.7%
p 16556
 
4.5%
t 12362
 
3.3%
c 11802
 
3.2%
Other values (18) 101108
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 370852
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 51961
14.0%
e 47733
12.9%
r 33954
 
9.2%
i 26890
 
7.3%
n 23945
 
6.5%
a 23570
 
6.4%
o 20971
 
5.7%
p 16556
 
4.5%
t 12362
 
3.3%
c 11802
 
3.2%
Other values (18) 101108
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 370852
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 51961
14.0%
e 47733
12.9%
r 33954
 
9.2%
i 26890
 
7.3%
n 23945
 
6.5%
a 23570
 
6.4%
o 20971
 
5.7%
p 16556
 
4.5%
t 12362
 
3.3%
c 11802
 
3.2%
Other values (18) 101108
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 370852
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 51961
14.0%
e 47733
12.9%
r 33954
 
9.2%
i 26890
 
7.3%
n 23945
 
6.5%
a 23570
 
6.4%
o 20971
 
5.7%
p 16556
 
4.5%
t 12362
 
3.3%
c 11802
 
3.2%
Other values (18) 101108
27.3%
Distinct3788
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size400.8 KiB
2025-10-14T10:22:25.475832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length127
Median length89
Mean length30.856931
Min length5

Characters and Unicode

Total characters1582652
Distinct characters85
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98 ?
Unique (%)0.2%

Sample

1st rowTenex Lockers, Blue
2nd rowAcme Trimmer, High Speed
3rd rowTenex Box, Single Width
4th rowEnermax Note Cards, Premium
5th rowEldon Light Bulb, Duo Pack
ValueCountFrequency (%)
labels 2385
 
1.0%
recycled 2291
 
1.0%
color 2187
 
0.9%
with 2177
 
0.9%
set 2106
 
0.9%
blue 2092
 
0.9%
durable 2072
 
0.9%
black 2055
 
0.9%
avery 1920
 
0.8%
clear 1893
 
0.8%
Other values (2826) 210320
90.9%
2025-10-14T10:22:26.489240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
179838
 
11.4%
e 154618
 
9.8%
a 94421
 
6.0%
r 91563
 
5.8%
o 88370
 
5.6%
l 79902
 
5.0%
i 79392
 
5.0%
n 68089
 
4.3%
t 62491
 
3.9%
s 60638
 
3.8%
Other values (75) 623330
39.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1582652
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
179838
 
11.4%
e 154618
 
9.8%
a 94421
 
6.0%
r 91563
 
5.8%
o 88370
 
5.6%
l 79902
 
5.0%
i 79392
 
5.0%
n 68089
 
4.3%
t 62491
 
3.9%
s 60638
 
3.8%
Other values (75) 623330
39.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1582652
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
179838
 
11.4%
e 154618
 
9.8%
a 94421
 
6.0%
r 91563
 
5.8%
o 88370
 
5.6%
l 79902
 
5.0%
i 79392
 
5.0%
n 68089
 
4.3%
t 62491
 
3.9%
s 60638
 
3.8%
Other values (75) 623330
39.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1582652
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
179838
 
11.4%
e 154618
 
9.8%
a 94421
 
6.0%
r 91563
 
5.8%
o 88370
 
5.6%
l 79902
 
5.0%
i 79392
 
5.0%
n 68089
 
4.3%
t 62491
 
3.9%
s 60638
 
3.8%
Other values (75) 623330
39.4%

sales
Real number (ℝ)

High correlation 

Distinct2246
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean246.49844
Minimum0
Maximum22638
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size400.8 KiB
2025-10-14T10:22:26.710403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q131
median85
Q3251
95-th percentile1016
Maximum22638
Range22638
Interquartile range (IQR)220

Descriptive statistics

Standard deviation487.56717
Coefficient of variation (CV)1.9779727
Kurtosis176.72302
Mean246.49844
Median Absolute Deviation (MAD)67
Skewness8.1379805
Sum12642905
Variance237721.75
MonotonicityNot monotonic
2025-10-14T10:22:27.064168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 589
 
1.1%
11 550
 
1.1%
14 527
 
1.0%
19 523
 
1.0%
17 520
 
1.0%
15 518
 
1.0%
12 512
 
1.0%
16 505
 
1.0%
9 498
 
1.0%
22 478
 
0.9%
Other values (2236) 46070
89.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 44
 
0.1%
2 142
 
0.3%
3 274
0.5%
4 306
0.6%
5 372
0.7%
6 433
0.8%
7 411
0.8%
8 413
0.8%
9 498
1.0%
ValueCountFrequency (%)
22638 1
< 0.1%
17500 1
< 0.1%
14000 1
< 0.1%
11200 1
< 0.1%
10500 1
< 0.1%
9893 1
< 0.1%
9450 1
< 0.1%
9100 1
< 0.1%
8750 1
< 0.1%
8400 1
< 0.1%

quantity
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4765451
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size400.8 KiB
2025-10-14T10:22:27.287735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum14
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2787663
Coefficient of variation (CV)0.65546864
Kurtosis2.2758887
Mean3.4765451
Median Absolute Deviation (MAD)1
Skewness1.3603677
Sum178312
Variance5.1927759
MonotonicityNot monotonic
2025-10-14T10:22:27.472508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 12748
24.9%
3 9682
18.9%
1 8963
17.5%
4 6385
12.4%
5 4882
 
9.5%
6 3020
 
5.9%
7 2385
 
4.7%
8 1361
 
2.7%
9 987
 
1.9%
10 276
 
0.5%
Other values (4) 601
 
1.2%
ValueCountFrequency (%)
1 8963
17.5%
2 12748
24.9%
3 9682
18.9%
4 6385
12.4%
5 4882
 
9.5%
6 3020
 
5.9%
7 2385
 
4.7%
8 1361
 
2.7%
9 987
 
1.9%
10 276
 
0.5%
ValueCountFrequency (%)
14 186
 
0.4%
13 83
 
0.2%
12 176
 
0.3%
11 156
 
0.3%
10 276
 
0.5%
9 987
 
1.9%
8 1361
 
2.7%
7 2385
4.7%
6 3020
5.9%
5 4882
9.5%

discount
Real number (ℝ)

High correlation  Zeros 

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14290755
Minimum0
Maximum0.85
Zeros29009
Zeros (%)56.6%
Negative0
Negative (%)0.0%
Memory size400.8 KiB
2025-10-14T10:22:27.666747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.2
95-th percentile0.6
Maximum0.85
Range0.85
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.21227993
Coefficient of variation (CV)1.4854354
Kurtosis0.71668241
Mean0.14290755
Median Absolute Deviation (MAD)0
Skewness1.3877746
Sum7329.728
Variance0.045062769
MonotonicityNot monotonic
2025-10-14T10:22:27.918571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 29009
56.6%
0.2 4998
 
9.7%
0.1 4068
 
7.9%
0.4 3177
 
6.2%
0.6 2006
 
3.9%
0.7 1786
 
3.5%
0.5 1633
 
3.2%
0.17 735
 
1.4%
0.47 725
 
1.4%
0.15 541
 
1.1%
Other values (17) 2612
 
5.1%
ValueCountFrequency (%)
0 29009
56.6%
0.002 461
 
0.9%
0.07 150
 
0.3%
0.1 4068
 
7.9%
0.15 541
 
1.1%
0.17 735
 
1.4%
0.2 4998
 
9.7%
0.202 41
 
0.1%
0.25 198
 
0.4%
0.27 388
 
0.8%
ValueCountFrequency (%)
0.85 2
 
< 0.1%
0.8 316
 
0.6%
0.7 1786
3.5%
0.65 17
 
< 0.1%
0.602 23
 
< 0.1%
0.6 2006
3.9%
0.57 12
 
< 0.1%
0.55 10
 
< 0.1%
0.5 1633
3.2%
0.47 725
 
1.4%

profit
Real number (ℝ)

High correlation  Zeros 

Distinct24575
Distinct (%)47.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.64174
Minimum-6599.978
Maximum8399.976
Zeros668
Zeros (%)1.3%
Negative12543
Negative (%)24.5%
Memory size400.8 KiB
2025-10-14T10:22:28.241863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-6599.978
5-th percentile-83.87784
Q10
median9.24
Q336.81
95-th percentile211.5
Maximum8399.976
Range14999.954
Interquartile range (IQR)36.81

Descriptive statistics

Standard deviation174.42411
Coefficient of variation (CV)6.0898575
Kurtosis290.90013
Mean28.64174
Median Absolute Deviation (MAD)15.96
Skewness4.1581878
Sum1469034.8
Variance30423.771
MonotonicityNot monotonic
2025-10-14T10:22:28.591457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 668
 
1.3%
4.32 70
 
0.1%
3.96 69
 
0.1%
7.92 67
 
0.1%
2.64 63
 
0.1%
2.88 60
 
0.1%
6.84 57
 
0.1%
9 56
 
0.1%
0.48 55
 
0.1%
3.42 55
 
0.1%
Other values (24565) 50070
97.6%
ValueCountFrequency (%)
-6599.978 1
< 0.1%
-4088.376 1
< 0.1%
-3839.9904 1
< 0.1%
-3701.8928 1
< 0.1%
-3399.98 1
< 0.1%
-3059.82 1
< 0.1%
-3009.435 1
< 0.1%
-2929.4845 1
< 0.1%
-2750.28 1
< 0.1%
-2639.9912 1
< 0.1%
ValueCountFrequency (%)
8399.976 1
< 0.1%
6719.9808 1
< 0.1%
5039.9856 1
< 0.1%
4946.37 1
< 0.1%
4630.4755 1
< 0.1%
3979.08 1
< 0.1%
3919.9888 1
< 0.1%
3177.475 1
< 0.1%
2939.31 1
< 0.1%
2817.99 1
< 0.1%

shipping_cost
Real number (ℝ)

High correlation 

Distinct10037
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.375915
Minimum0
Maximum933.57
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size400.8 KiB
2025-10-14T10:22:28.891978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.61
Q12.61
median7.79
Q324.45
95-th percentile111.4095
Maximum933.57
Range933.57
Interquartile range (IQR)21.84

Descriptive statistics

Standard deviation57.296804
Coefficient of variation (CV)2.1723153
Kurtosis50.020158
Mean26.375915
Median Absolute Deviation (MAD)6.41
Skewness5.8632264
Sum1352820.7
Variance3282.9237
MonotonicityNot monotonic
2025-10-14T10:22:29.284340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.86 76
 
0.1%
1.26 75
 
0.1%
0.71 75
 
0.1%
1.36 74
 
0.1%
0.35 73
 
0.1%
0.79 71
 
0.1%
0.94 71
 
0.1%
1.04 71
 
0.1%
0.97 70
 
0.1%
0.69 70
 
0.1%
Other values (10027) 50564
98.6%
ValueCountFrequency (%)
0 2
 
< 0.1%
0.01 6
 
< 0.1%
0.02 9
 
< 0.1%
0.03 9
 
< 0.1%
0.04 14
< 0.1%
0.05 19
< 0.1%
0.06 18
< 0.1%
0.07 13
< 0.1%
0.08 17
< 0.1%
0.09 23
< 0.1%
ValueCountFrequency (%)
933.57 1
< 0.1%
923.63 1
< 0.1%
915.49 1
< 0.1%
910.16 1
< 0.1%
903.04 1
< 0.1%
897.35 1
< 0.1%
894.77 1
< 0.1%
878.38 1
< 0.1%
867.69 1
< 0.1%
865.74 1
< 0.1%

order_priority
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size400.8 KiB
Medium
29433 
High
15501 
Critical
3932 
Low
 
2424

Length

Max length8
Median length6
Mean length5.4070969
Min length3

Characters and Unicode

Total characters277330
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowMedium
3rd rowHigh
4th rowHigh
5th rowMedium

Common Values

ValueCountFrequency (%)
Medium 29433
57.4%
High 15501
30.2%
Critical 3932
 
7.7%
Low 2424
 
4.7%

Length

2025-10-14T10:22:29.546263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-14T10:22:29.772254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
medium 29433
57.4%
high 15501
30.2%
critical 3932
 
7.7%
low 2424
 
4.7%

Most occurring characters

ValueCountFrequency (%)
i 52798
19.0%
M 29433
10.6%
d 29433
10.6%
u 29433
10.6%
m 29433
10.6%
e 29433
10.6%
H 15501
 
5.6%
g 15501
 
5.6%
h 15501
 
5.6%
c 3932
 
1.4%
Other values (8) 26932
9.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 277330
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 52798
19.0%
M 29433
10.6%
d 29433
10.6%
u 29433
10.6%
m 29433
10.6%
e 29433
10.6%
H 15501
 
5.6%
g 15501
 
5.6%
h 15501
 
5.6%
c 3932
 
1.4%
Other values (8) 26932
9.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 277330
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 52798
19.0%
M 29433
10.6%
d 29433
10.6%
u 29433
10.6%
m 29433
10.6%
e 29433
10.6%
H 15501
 
5.6%
g 15501
 
5.6%
h 15501
 
5.6%
c 3932
 
1.4%
Other values (8) 26932
9.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 277330
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 52798
19.0%
M 29433
10.6%
d 29433
10.6%
u 29433
10.6%
m 29433
10.6%
e 29433
10.6%
H 15501
 
5.6%
g 15501
 
5.6%
h 15501
 
5.6%
c 3932
 
1.4%
Other values (8) 26932
9.7%

year
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size400.8 KiB
2014
17531 
2013
13799 
2012
10962 
2011
8998 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters205160
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2011
2nd row2011
3rd row2011
4th row2011
5th row2011

Common Values

ValueCountFrequency (%)
2014 17531
34.2%
2013 13799
26.9%
2012 10962
21.4%
2011 8998
17.5%

Length

2025-10-14T10:22:29.955976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-14T10:22:30.183364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2014 17531
34.2%
2013 13799
26.9%
2012 10962
21.4%
2011 8998
17.5%

Most occurring characters

ValueCountFrequency (%)
2 62252
30.3%
1 60288
29.4%
0 51290
25.0%
4 17531
 
8.5%
3 13799
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 205160
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 62252
30.3%
1 60288
29.4%
0 51290
25.0%
4 17531
 
8.5%
3 13799
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 205160
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 62252
30.3%
1 60288
29.4%
0 51290
25.0%
4 17531
 
8.5%
3 13799
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 205160
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 62252
30.3%
1 60288
29.4%
0 51290
25.0%
4 17531
 
8.5%
3 13799
 
6.7%

Interactions

2025-10-14T10:22:11.948703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T10:22:06.890845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T10:22:08.176180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T10:22:09.318259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T10:22:10.558012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T10:22:12.220294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T10:22:07.150953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T10:22:08.379790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T10:22:09.565961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T10:22:10.863262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T10:22:12.421497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T10:22:07.389210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T10:22:08.612830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T10:22:09.808371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T10:22:11.154736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T10:22:12.637716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T10:22:07.621735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T10:22:08.820298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T10:22:10.079777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T10:22:11.397197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T10:22:12.869980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T10:22:07.898416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T10:22:09.085648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T10:22:10.329814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T10:22:11.648187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-14T10:22:30.439957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
categorydiscountmarketorder_priorityprofitquantityregionsalessegmentship_modeshipping_costsub_categoryyear
category1.0000.1850.0730.0030.0560.0150.0560.0620.0000.0000.1561.0000.002
discount0.1851.0000.3480.011-0.5960.0180.335-0.1000.0000.019-0.0940.1590.020
market0.0730.3481.0000.0130.0100.1570.8820.0210.0090.0140.0320.1280.028
order_priority0.0030.0110.0131.0000.0090.0080.0270.0000.0190.2840.0990.0040.017
profit0.056-0.5960.0100.0091.0000.2020.0100.4900.0000.0110.4500.0580.000
quantity0.0150.0180.1570.0080.2021.0000.1290.4160.0000.0080.3790.0160.010
region0.0560.3350.8820.0270.0100.1291.0000.0190.0120.0260.0280.0690.022
sales0.062-0.1000.0210.0000.4900.4160.0191.0000.0000.0000.9130.0610.000
segment0.0000.0000.0090.0190.0000.0000.0120.0001.0000.0110.0000.0120.014
ship_mode0.0000.0190.0140.2840.0110.0080.0260.0000.0111.0000.0740.0110.007
shipping_cost0.156-0.0940.0320.0990.4500.3790.0280.9130.0000.0741.0000.1120.014
sub_category1.0000.1590.1280.0040.0580.0160.0690.0610.0120.0110.1121.0000.005
year0.0020.0200.0280.0170.0000.0100.0220.0000.0140.0070.0140.0051.000

Missing values

2025-10-14T10:22:13.341701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-14T10:22:14.043020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

order_idorder_dateship_dateship_modecustomer_namesegmentstatecountrymarketregionproduct_idcategorysub_categoryproduct_namesalesquantitydiscountprofitshipping_costorder_priorityyear
0AG-2011-20402011-01-01 00:00:002011-06-01 00:00:00Standard ClassToby BraunhardtConsumerConstantineAlgeriaAfricaAfricaOFF-TEN-10000025Office SuppliesStorageTenex Lockers, Blue40820.0106.14035.46Medium2011
1IN-2011-478832011-01-01 00:00:002011-08-01 00:00:00Standard ClassJoseph HoltConsumerNew South WalesAustraliaAPACOceaniaOFF-SU-10000618Office SuppliesSuppliesAcme Trimmer, High Speed12030.136.0369.72Medium2011
2HU-2011-12202011-01-01 00:00:002011-05-01 00:00:00Second ClassAnnie ThurmanConsumerBudapestHungaryEMEAEMEAOFF-TEN-10001585Office SuppliesStorageTenex Box, Single Width6640.029.6408.17High2011
3IT-2011-36476322011-01-01 00:00:002011-05-01 00:00:00Second ClassEugene MorenHome OfficeStockholmSwedenEUNorthOFF-PA-10001492Office SuppliesPaperEnermax Note Cards, Premium4530.5-26.0554.82High2011
4IN-2011-478832011-01-01 00:00:002011-08-01 00:00:00Standard ClassJoseph HoltConsumerNew South WalesAustraliaAPACOceaniaFUR-FU-10003447FurnitureFurnishingsEldon Light Bulb, Duo Pack11450.137.7704.70Medium2011
5IN-2011-478832011-01-01 00:00:002011-08-01 00:00:00Standard ClassJoseph HoltConsumerNew South WalesAustraliaAPACOceaniaOFF-PA-10001968Office SuppliesPaperEaton Computer Printout Paper, 8.5 x 115520.115.3421.80Medium2011
6CA-2011-15102011-02-01 00:00:002011-06-01 00:00:00Standard ClassMagdelene MorseConsumerOntarioCanadaCanadaCanadaTEC-OKI-10002750TechnologyMachinesOkidata Inkjet, Wireless31410.03.12024.10Medium2011
7IN-2011-793972011-03-01 00:00:002011-03-01 00:00:00Same DayKean NguyenCorporateNew South WalesAustraliaAPACOceaniaOFF-AP-10000304Office SuppliesAppliancesHoover Microwave, White27610.1110.412125.32Critical2011
8ID-2011-802302011-03-01 00:00:002011-09-01 00:00:00Standard ClassKen LonsdaleConsumerAucklandNew ZealandAPACOceaniaTEC-CO-10004182TechnologyCopiersHewlett Wireless Fax, Laser91240.4-319.464107.10Low2011
9IZ-2011-46802011-03-01 00:00:002011-07-01 00:00:00Standard ClassLindsay WilliamsCorporateNinawaIraqEMEAEMEAFUR-NOV-10002791FurnitureChairsNovimex Swivel Stool, Set of Two66740.0253.32081.26High2011
order_idorder_dateship_dateship_modecustomer_namesegmentstatecountrymarketregionproduct_idcategorysub_categoryproduct_namesalesquantitydiscountprofitshipping_costorder_priorityyear
51280TZ-2014-822031-12-20142015-06-01 00:00:00Standard ClassChristine KargatisHome OfficeDar Es SalaamTanzaniaAfricaAfricaOFF-HAR-10001531Office SuppliesLabelsHarbour Creations Removable Labels, Adjustable5060.06.84002.15Medium2014
51281CA-2014-11542731-12-20142015-04-01 00:00:00Standard ClassErica BernCorporateCaliforniaUnited StatesUSWestOFF-BI-10004632Office SuppliesBindersGBC Binding covers2120.26.47502.06Medium2014
51282UP-2014-441031-12-20142015-04-01 00:00:00Standard ClassGuy ThorntonConsumerZaporizhzhyaUkraineEMEAEMEAOFF-AVE-10003558Office SuppliesLabelsAvery Round Labels, Alphabetical2840.06.12001.70Medium2014
51283IN-2014-2375431-12-20142015-07-01 00:00:00Standard ClassKalyca MeadeCorporateGuangdongChinaAPACNorth AsiaOFF-PA-10004727Office SuppliesPaperEaton Note Cards, 8.5 x 117930.025.38001.41Medium2014
51284MX-2014-10857431-12-20142015-04-01 00:00:00Standard ClassJulia BarnettHome OfficeTamaulipasMexicoLATAMNorthOFF-LA-10004969Office SuppliesLabelsNovimex Legal Exhibit Labels, Adjustable1730.00.66001.32Medium2014
51285CA-2014-11542731-12-20142015-04-01 00:00:00Standard ClassErica BernCorporateCaliforniaUnited StatesUSWestOFF-BI-10002103Office SuppliesBindersCardinal Slant-D Ring Binder, Heavy Gauge Vinyl1420.24.51880.89Medium2014
51286MO-2014-256031-12-20142015-05-01 00:00:00Standard ClassLiz PreisConsumerSouss-Massa-DraâMoroccoAfricaAfricaOFF-WIL-10001069Office SuppliesBindersWilson Jones Hole Reinforcements, Clear410.00.42000.49Medium2014
51287MX-2014-11052731-12-20142015-02-01 00:00:00Second ClassCharlotte MeltonConsumerManaguaNicaraguaLATAMCentralOFF-LA-10004182Office SuppliesLabelsHon Color Coded Labels, 5000 Label Set2630.012.36000.35Medium2014
51288MX-2014-11478331-12-20142015-06-01 00:00:00Standard ClassTamara DahlenConsumerChihuahuaMexicoLATAMNorthOFF-LA-10000413Office SuppliesLabelsHon Legal Exhibit Labels, Alphabetical710.00.56000.20Medium2014
51289CA-2014-15672031-12-20142015-04-01 00:00:00Standard ClassJill MatthiasConsumerColoradoUnited StatesUSWestOFF-FA-10003472Office SuppliesFastenersBagged Rubber Bands330.2-0.60480.17Medium2014